# A Multi-Agent Systems Approach for Peer-to-Peer Energy Trading in Dairy Farming

arXiv: https://arxiv.org/abs/2310.05932 · HTML: https://mlsanity.com/paper/2310.05932/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 16 references that are in the map.
- First posted on arXiv 21 Aug 2023.

## Built on by (most cited first, 1 of 1)

- [Reinforcement Learning Enabled Peer-to-Peer Energy Trading for Dairy Farms](https://mlsanity.com/paper/2405.12716/) (2024)

## Abstract (arXiv)

To achieve desired carbon emission reductions, integrating renewable generation and accelerating the adoption of peer-to-peer energy trading is crucial. This is especially important for energy-intensive farming, like dairy farming. However, integrating renewables and peer-to-peer trading presents challenges. To address this, we propose the Multi-Agent Peer-to-Peer Dairy Farm Energy Simulator (MAPDES), enabling dairy farms to participate in peer-to-peer markets. Our strategy reduces electricity costs and peak demand by approximately 30% and 24% respectively, while increasing energy sales by 37% compared to the baseline scenario without P2P trading. This demonstrates the effectiveness of our approach.